FAQ

A traditional equity risk model decomposes returns using style factors such as value, momentum, and sector. A macro factor model maps returns directly to economic variables like interest rates, credit spreads, and inflation expectations at the individual security level. The distinction matters because macro-driven drawdowns often appear as unexplained residuals in traditional models.

No, and that is not the right framing. Macro factor models like MFERM complement existing risk stacks by adding daily macro-versus-idiosyncratic decomposition at the single-stock level, which Barra and Axioma do not provide as their primary capability.

MFERM decomposes each security's daily return into a macro-driven component and an idiosyncratic component. The idiosyncratic return, after stripping out macro factor exposure, represents the stock-specific return attributable to fundamentals and genuine alpha generation rather than macro tailwinds.

Qi treats macro variables as external forces that influence asset prices independently of company fundamentals. Our platform uses Partial Least Squares Regression (PLSR) to solve for how sensitive each security is to macro factors like GDP growth, interest rates, and credit spreads. This provides both post-trade risk analysis through Macro Risk tools (MFERM) and pre-trade valuation insights through our Macro Valuation tools.

No.

Qi complements traditional style factor models. While Axioma decomposes risk into style factors, Qi decomposes risk into macro factors. One can connect style and macro by using Qi to reveal the macro forces driving style and other thematic factors. Qi can also be used“side by side” with traditional equity fundamental factor models. It provides a macro lens to view your overall portfolio exposure and risk. Most clients use both. This dual approach provides deeper insights into portfolio behavior, especially during regime shifts.

They complement each other within your risk process.

Use both for comprehensive risk intelligence. Style models identify your exposures to factors like value and momentum; Qi explains your portfolio in terms of macro factors. Qi can also show the macro drivers of style factors. For example, knowing your portfolio has high momentum exposure is valuable—understanding what macro conditions drive momentum's performance is transformative.

Traditional models like Barra start with known exposures and estimate factor returns. Qi does the opposite—we start with observed macro factor returns and estimate each security's exposure to those factors. This approach powers both our risk attribution capabilities and our fair value analysis for individual securities.

No, Qi complements style models. While style models show your value, momentum, and growth tilts, our macro analytics reveal how macro forces drive those style returns and individual security valuations. Used together, they provide a complete picture of what's driving performance and where opportunities exist.

Our selection process balances rigor with relevance:

• Economic significance (clear financial theory connection)
• Statistical validation (persistent explanatory power)
• Independence (minimal overlap between factors)
• Cross-asset relevance (explanatory power across markets)
• Stability analysis (predictive value across regimes)

Qi treats macro variables as exogenous toindividual securities—meaning macroeconomic trends exist independently of the company’s internal fundamentals but still exert significant influence over asset prices. While equities possess fundamental drivers such as earnings growth, margins,and competitive positioning, they do not inherently have macro traits.

To capture the impact of macro forces, Qi employs a robust Partial Least Squares Regression (PLSR) algorithm, which is specifically designed to address the high multicollinearity often found among macroeconomic variables. This statistical approach ensures stable and intuitive estimates of portfolio sensitivities,even when factors are highly correlated. Factor selection is guided by a seasoned team of macro strategists, portfolio managers, and data scientists, ensuring the inclusion of both economically meaningful and empirically robust variables.

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Even the most rigorous bottom-up investment process is influenced—sometimes significantly—by the macroeconomic backdrop. Valuation multiples expand or contract as interest rates and growth expectations shift. Earnings quality, growth sustainability, and competitive dynamics are all affected by macro tailwinds or headwinds. Most portfolios—whether concentrated or diversified—contain unintentional macro exposures, such as sensitivity to the yield curve, inflation expectations, or commodity prices. Qi’s analysis of hundreds of fundamental equity portfolios has shown that 50–80% of quarterly returns can be explained by macro factors. Understanding and managing these exposures does not diminish a manager’s fundamental edge; instead, it sharpens it by isolating genuine alpha from macro-driven noise and allowing managers to make more deliberate risk-reward decisions.

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Our analysis of hundreds of equity portfolios shows that 50-80% of quarterly returns can be explained by macro factors. Even the best stock-picking process is influenced by the macro backdrop—interest rates affect valuations, growth expectations drive multiples. Qi helps you separate genuine alpha from macro-driven performance and identify when securities are mispriced relative to macro fundamentals.

Our platform enables you to monitor and control macro risk in portfolios while identifying mispriced securities. You can set macro risk limits, time gross exposure adjustments, use fair value gaps for entry/exit timing, and ensure idiosyncratic alpha isn't eroded by unintended macro bets. This helps avoid drawdowns during macro shocks while capturing valuation opportunities.

Yes. By systematically reducing Macro Share of Risk (MSR) and using our fair value analysis for better entry points, our platform helps make portfolios more resilient and better positioned. This protects against downside while enabling you to stay invested in attractively valued positions during market stress.

MFERM enables long/short managers toexplicitly monitor and control macro risk within their portfolios. By quantifying sensitivities to key macro factors, managers can set explicit macro risk limits, ensure that idiosyncratic alpha is not eroded by unintended macro bets, and time adjustments to gross and net exposures based on changes in the macro risk environment. This is particularly valuable in avoiding drawdowns during adverse macro shocks without unnecessarily cutting positions that still have strong fundamental merit.

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Our models use variance-covariance matrices with 90-day half-lives to forecast portfolio volatility and fair value ranges based on macro exposures. The Macro Share of Risk (MSR) metric inversely correlates with forward Sharpe ratios, while our Fair Value Gaps help identify mean-reversion opportunities in individual securities.

Qi insights translate directly into investment decisions:

• Adjust allocations based on changing macro sensitivities
• Use fair value gaps for timing entry and exit points
• Hedge specific macro risks rather than broad de-risking
• Build balanced portfolios avoiding concentrated macro bets
• Identify securities trading away from macro-justified levels

Our data, insights and analysis translate directly into portfolio actions in variety of ways.
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Managers can:
- Adjust tactical allocations based on changing macro sensitivities
- Hedge specific macro risks rather than broadly de-risking
- Time entries and exits based on regime awareness
- Build balanced portfolios that avoid concentrated macro bets
- Allocate risk budgets to high-conviction themes while controlling unintended exposures

Example:
before the 2022 slowdown, a client identified heightened growth sensitivity and implemented targeted sector rotations and overlay hedges. This reduced drawdowns by 40% relative to their benchmark while preserving alpha opportunities.

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The dataset covers 18,000+ securities across equities and multi-asset classes. Both the MFERM and the Macro Valuation engine update daily, giving portfolio managers and risk teams a current view of macro exposures and macro-implied fair values.

We group factors into three categories: Growth Expectations (GDP nowcasts, PMIs), Financial Conditions (yields, credit spreads, FX, commodities), and Risk Appetite (VIX, volatility measures). Factors are selected for economic significance, statistical persistence, and stability across market regimes.

Our analytics are available via API feeds, web interface, daily file drops, and through partner platforms including Omega Point, EDS, and GS Marquee. We integrate easily with existing systems and provide both risk attribution and valuation analysis in unified workflows.

Most clients integrate Qi through:

• A dedicated "Macro Risk" section showing factor sensitivities and contributions
• Enhanced attribution that includes macro alongside traditional breakdowns
• Scenario analysis showing potential impacts from specific macro shifts
• Risk alerts when factor relationships deviate from historical patterns

We provide templates aligned with standard risk frameworks, ensuring
seamless integration without disrupting existing workflows.